A harness needs to be good. Being large is annoying, sure, but it's generally not an issue. Unless this one has feature parity with the "large" ones (hint: it doesn't), I don't see any reason to use it only because it's small.
I ported almost every key feature already tbh. Of course there will always be a compromise, but there is even still margin to add more. Tools, skills, context compaction, it’s all already implemented.
I haven't actually tried it, my comment was based on the "Known bugs and issues" section in the readme (e.g. no MCP support yet). Apologies if that section is out of date. If the functionality is on par with the mainstream ones, then sure, small and lightweight is preferable.
ultimately, the harness is just going to be another model specifically engineered to be deterministic, like ballbearings in a wheel minimizing friction.
Probably you want all of them to be as simple and small as possible, especially if you want to use it to work on itself. Smaller codebases are (generally) easier to work with.
I think it's nice to have options, but I also don't see the appeal in "sub x mb" (except as codegolf or just for the hacky part, for day to day this does not matter) especially if features are missing or other compromises have been made just for the sake of remaining small.
What about running agents on embedded devices with small resources? maybe even multi agents flows? good luck trying to do that with codex, let alone claude code
i love the idea, but you also need to maintain it long term: in today's world what is the best practice to do that... do you spin up an agent anytime codex-cli updates and mirror the updates in c++ and push a new release? or idk... set up an automated process that does this on a worker on CF in a sandbox when npm updates @openai/codex?
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